Integrated Chinese and Western Medicine Prognosis Model of Complications in Patients with Influenza A/B or COVID-19
10.13422/j.cnki.syfjx.20241991
- VernacularTitle:呼吸道传染性疾病中甲乙流新冠病毒感染患者出现并发症中西医结合预后模型探讨
- Author:
Ze XU
1
;
Qun LIANG
1
Author Information
1. The First Affiliated Hospital of Heilongjiang University of Chinese Medicine，Harbin 150000，China
- Publication Type:Journal Article
- Keywords:
complications of respiratory viral infections;
risk prediction model;
traditional Chinese medicine;
nomogram
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2024;30(19):144-153
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveBased on real-world clinical data of traditional Chinese medicine （TCM）， a Cox proportional hazards model was built to predict the risk factors of complications in patients with Corona Virus Disease 2019 （COVID-19） or influenza A/B， and the cumulative occurrence function graph was used to present the prediction output. MethodThe medical records of the patients with respiratory infectious diseases， including COVID-19 and influenza A/B， treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from November 2022 to October 2023 were collected. The data from the electronic medical record system were integrated into a data warehouse. The information of the patients with respiratory diseases caused by influenza A and B viruses and SARS-CoV-2 from November 2022 to October 2023 was retrospectively collected. The information involved age， gender， disease course， past medical history， laboratory test results， tongue manifestation， pulse manifestation， TCM syndrome， and main therapeutic drugs. The outcome indicators of whether complications occurred were obtained by telephone follow-up and review of readmission records. The data was divided into a training set and a validation set in a ratio of 70% and 30%， respectively. In the training set， the Cox proportional hazards model was used to identify the key factors affecting patient complications. Then， the combination of variables was optimized by stepwise elimination method， and an efficient complication risk assessment model was constructed， which was visualized in the form of histogram. The C-index， receiver operating characteristic （ROC） curve， calibration error graph， and decision curve analysis were employed to comprehensively measure the prediction performance of the model. ResultThe history of chronic lung diseases ［hazard ratio （HR） 4.46， 95% confidence interval （95%CI） 1.79-11.12］， Qi deficiency （HR 5.74， 95%CI 2.14-15.39）， thready and weak pulse （HR 4.45， 95%CI 1.88-10.50）， hormone use history （HR 4.57， 95%CI 2.04-10.23）， procalcitonin （PCT>10 μg·L-1） （HR 1.23， 95%CI 0.06-0.86）， serum amyloid A （SAA）>100 mg·L-1 （HR 9.80， 95%CI 7.24-59.75）， and platelet （PLT）>303×109 /L （HR 5.66， 95%CI 2.01-16.00） were the risk factors for complications. Chinese medicine intervention （HR 0.20， 95%CI 0.06-0.70） was the protective factor for complications. Based on the above risk factors， the prediction model was constructed. In the training set， the C-index was estimated to be 0.765， and the CI was within the range of 0.667 to 0.859. In the validation set， the C-index was 0.804， and the CI varied within the range of 0.773 to 0.855. The temporal variation graph of C-index was then described. The area under the ROC curve （AUC） at 5， 10， 15 months was 0.61， 0.72， and 0.79 in the training set and 0.60， 0.67， and 0.62 in the validation set， respectively. In addition， calibration and decision curves were drawn for 5， 10， 15 months for both training and validation sets， which showed that the model had good calibration performance and was effective in clinical practice. ConclusionThe history of chronic lung diseases， Qi deficiency， thready and weak pulse， hormone use history， PCT>10 μg·L-1， SAA>100 mg·L-1， and PLT>303×109 /L were risk factors for complications in patients with COVID-19 or influenza A/B， while Chinese medicine intervention was a protective factor. The prediction model was established based on the indicators above. The model showcased excellent distinguishing performance， calibration performance， and clinical practicability， providing scientific support for the prevention and control of complications caused by respiratory viral infections.
